Naeini et al. (2026) Projecting Hurricane Risk in Atlantic Canada under Climate Change

Identification

• Journal: Weather and Climate Extremes
• Year: 2026
• Date: 2026-04-01
• Authors: Saeed Saviz Naeini, Reda Snaiki, Alejandro Di Luca
• DOI: 10.1016/j.wace.2026.100897

Research Groups

• Department of Construction Engineering, École de Technologie Supérieure, Université du Québec, Montréal, Québec, Canada
• Département des Sciences de la Terre et de l’Atmosphère, Centre pour l’étude et la Simulation du climat à l’échelle régionale (ESCER), Université du Québec à Montréal, Montréal, Québec, Canada

Short Summary

This study projects future tropical cyclone (TC) risk in Atlantic Canada under climate change, quantifying the evolution of wind and coastal flood hazards and associated economic losses. Findings indicate an intensification of wind extremes and substantial coastal inundation amplification, leading to higher wind-proxy risk for many coastal communities.

Objective

• To quantify the magnitude and spatial distribution of evolving tropical cyclone (TC) wind and coastal flood hazards and the resulting total TC risk in Atlantic Canada under climate change, providing decision-ready evidence for proactive adaptation planning.

Study Configuration

• Spatial Scale: Coastal regions of Atlantic Canada, encompassing Nova Scotia, New Brunswick, Prince Edward Island, Newfoundland and Labrador, and the Magdalen Islands in Quebec. Detailed analysis for five representative locations (Halifax, St. John’s, Saint John, Charlottetown, Magdalen Islands). Hazard maps generated at grid resolution, and flood modeling at approximately 30 meters by 55 meters resolution.
• Temporal Scale: Historical baseline (1979–2014), near future (2024–2059), and far future (2060–2095). Each period was simulated for 10,000 years of synthetic TC activity.

Methodology and Data

• Models used:
  • Physics-informed synthetic TC track model (comprising genesis, translation using beta-and-advection framework, and intensity modules, with FAST model for intensity).
  • Analytical wind field model (using Holland pressure profile model and empirical conversion factors for surface wind speed).
  • Bathtub modeling approach for coastal flood hazard.
  • Empirical wind-surge relationship (Xu, 2010) for storm surge estimation.
  • Adapted Emanuel-type vulnerability function for damage ratio calculation.
• Data sources:
  • ERA5 reanalysis for historical environmental conditions and as a baseline for future climate perturbations.
  • CMIP6 Global Climate Models (GCMs): EC-Earth3P-HR and CMCC-CM2-SR5 under the SSP5-8.5 high-emission scenario for future climate change signals.
  • Canadian relative sea-level projections adapted from IPCC Sixth Assessment Report (SSP5-8.5).
  • High-Resolution Digital Elevation Model (HRDEM) from Natural Resources Canada (1 meter × 1 meter spatial resolution, CGVD2013).
  • 2020 NASA BlackMarble dataset (1 kilometer resolution) as a proxy for population distribution.
  • Provincial Gross Domestic Product (GDP) per capita data from the World Bank (2020 Canadian dollars) for economic exposure scaling.
  • World Meteorological Organization (WMO) dataset for land cover to derive surface wind reduction factors.
  • National Building Code of Canada (NBCC) Appendix C for regional wind speed consistency checks.

Main Results

• A consistent and notable decrease in storm frequency (ranging from 11% to 27%) was projected across selected locations and GCM scenarios.
• Wind hazard intensified across all return periods, with the most pronounced increases observed in the far-future period (2060-2095).
• 100-year wind speeds are projected to increase by up to 26% (EC-Earth3P-HR model) in the far-future, particularly along the coasts of Newfoundland, New Brunswick, and Nova Scotia. The CMCC-CM2-SR5 model showed a more mixed response, with changes ranging from -7% to +22%.
• Sea Level Rise (SLR) was identified as the dominant factor amplifying future coastal flood hazard. For example, at Halifax, the 100-year flood depth in the far future increased by 11% due to storm changes alone, but by 48% when SLR was included.
• Total economic risk, quantified as Expected Damage (ED) from wind-proxy losses, showed an upward trend, with the highest absolute damages projected for urbanized coastal communities in Nova Scotia and Newfoundland.
• Significant model uncertainty was observed, with the EC-Earth3P-HR model consistently projecting higher damage increases (e.g., a 77% increase for a 300-year event in Prince Edward Island in the far-future), while the CMCC-CM2-SR5 model showed more varied increases and decreases (e.g., a 2% decrease for the same event in Prince Edward Island).
• Future hazard changes are primarily driven by increases in storm intensity rather than changes in encounter frequency.

Contributions

• Provides a comprehensive, quantitative assessment of evolving tropical cyclone (TC) wind and coastal flood hazards and associated economic risk for Atlantic Canada under climate change.
• Characterizes the non-stationary nature of future TC behavior, explicitly quantifying changes across historical, near-future, and far-future periods.
• Clarifies the link between physical drivers (wind, storm surge, sea-level rise) and potential loss, using a wind-proxy methodology compatible with engineering and insurance practices.
• Identifies and maps future risk hotspots, offering decision-ready evidence for spatially targeted adaptation planning.
• Highlights the dominant role of sea-level rise in amplifying coastal flood hazard, even with potential decreases in storm frequency.
• Emphasizes the importance of considering GCM-driven model uncertainty in future risk evaluations.

Funding

• Natural Sciences and Engineering Research Council of Canada (NSERC) [grant number CRSNG RGPIN 2022-03492]
• AdapT – Institut de recherche sur les infrastructures résilientes et circulaires

Citation

@article{Naeini2026Projecting,
  author = {Naeini, Saeed Saviz and Snaiki, Reda and Luca, Alejandro Di},
  title = {Projecting Hurricane Risk in Atlantic Canada under Climate Change},
  journal = {Weather and Climate Extremes},
  year = {2026},
  doi = {10.1016/j.wace.2026.100897},
  url = {https://doi.org/10.1016/j.wace.2026.100897}
}